A wireless sensor network (hereinafter referred to as “WSN”) is a network of sensor nodes. The WSN is distributed in nature and is an event-based system. Due to the size and battery power limitation, sensor nodes typically have limited storage capability, limited resources and limited network bandwidth. These limitations of the sensor nodes demand specialized optimization techniques.
Typical WSN applications with a large number of sensor nodes are covered over a specific target area in close proximity of each other. In such deployments, spatial correlation of data is observed where neighboring sensor nodes report sensor data with a high degree of correlation. Similarly, temporal correlation of data is also observed in sensed environment data, where successive sensed data are found to be identical and vary slowly, except in the case of unexpected events. Moreover, there is redundancy in the sensor data. The communication cost imposed due to redundant data unnecessarily consumes lifetime of the sensor nodes as also the bandwidth.
In such a scenario, in a typical WSN, a large amount of information (spatial or temporal) can be combined together and represented by the same number of bits. This process is known as data aggregation. The aggregation mechanism can be lossless or lossy. In a lossless aggregation, more information is embedded in a single packet, thereby combining all headers into a single header and same data bits. In lossy aggregation, several pieces of information are passed through an aggregation function that generates a single packet having no details about the original information. Each aggregation mechanism has its own inherent drawbacks.
Research has been carried out in the art to address such drawbacks by optimizing the data aggregation mechanism. One approach includes enhance performance issues of sensor data aggregation for delaying energy trade-off in the presence of non-trivial aggregation. Another approach uses a fixed amount of time or packets to be aggregated in the aggregation mechanism. Still another approach teaches energy-accuracy tradeoff in which either a snapshot aggregation or a periodic aggregation is performed.
Several research works in the literature have discussed the problems and approaches of developing data aggregation processes mainly for energy, bandwidth and memory space savings by minimizing the data transferred in WSN. However, the proposed mechanisms in the art are too complex to be implemented in hardware using the current state of the art.
Thus all the techniques mentioned above in the art have one or more limitations. Accordingly, there is a need in the art for designing a data aggregation mechanism, which a) provides quality of service in data aggregation, b) is adaptive, and c) provides proper scheduling.